示例#1
0
文件: __init__.py 项目: zwq1230/GASDA
def create_train_dataloader(args):
    joint_transform_list = [RandomImgAugment(args.no_flip,args.no_rotation, args.no_augment, args.loadSize)]
    img_transform_list = [ToTensor(), Normalize([.5, .5, .5], [.5, .5, .5])]

    joint_transform = Compose(joint_transform_list)
    
    img_transform = Compose(img_transform_list)

    depth_transform = Compose([DepthToTensor()])

    src_dataset = get_dataset(root=args.src_root, data_file=args.src_train_datafile, phase='train',
                            dataset=args.src_dataset,
                            img_transform=img_transform, depth_transform=depth_transform,
                            joint_transform=joint_transform)

        
        
    tgt_dataset = get_dataset(root=args.tgt_root, data_file=args.tgt_train_datafile, phase='train',
                            dataset=args.tgt_dataset,
                            img_transform=img_transform, joint_transform=joint_transform,
                            depth_transform=depth_transform)

    loader = torch.utils.data.DataLoader(
                                ConcatDataset(
                                    src_dataset,
                                    tgt_dataset,
                                ),
                                batch_size=args.batchSize, shuffle=True,
                                num_workers=int(args.nThreads),
                                pin_memory=True)

    return loader
示例#2
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def create_test_dataloader(args):
    joint_transform_list = [RandomImgAugment(args.loadSize)]
    img_transform_list = [ToTensor(), Normalize([.5, .5, .5], [.5, .5, .5])]

    joint_transform = Compose(joint_transform_list)

    img_transform = Compose(img_transform_list)

    depth_transform = Compose([DepthToTensor()])

    dataset = get_dataset(root=args.root,
                          data_file=args.test_datafile,
                          phase='test',
                          dataset=args.tgt_dataset,
                          img_transform=img_transform,
                          joint_transform=joint_transform,
                          depth_transform=None,
                          test_dataset=args.test_dataset)
    loader = torch.utils.data.DataLoader(dataset,
                                         batch_size=1,
                                         shuffle=False,
                                         num_workers=int(args.nThreads),
                                         pin_memory=True)

    return loader
示例#3
0
def create_test_dataloader(args):

    joint_transform_list = [RandomImgAugment(True, True, Image.BICUBIC)]

    joint_transform = Compose(joint_transform_list)

    dataset = get_dataset(root=args.root,
                          data_file=args.test_data_file,
                          phase='test',
                          dataset=args.dataset,
                          joint_transform=joint_transform)
    loader = torch.utils.data.DataLoader(dataset,
                                         batch_size=1,
                                         shuffle=False,
                                         num_workers=int(args.nThreads),
                                         pin_memory=True)

    return loader